Paren van restaurants

In de vorige les heb je de restaurants-gegevensset opgeschoond om die klaar te maken voor het bouwen van een aanbevelingsengine voor restaurants. Je hebt een nieuw DataFrame restaurants_new met nieuwe restaurants om je model op te trainen, dat is gescraped uit een nieuwe bron.

Je hebt de kolommen cuisine_type en city al schoongemaakt met de technieken uit de cursus. Maar je zag duplicaten met typfouten in restaurantnamen die recordlinkage vereisen in plaats van joins met restaurants.

In deze oefening voer je de eerste stap van recordlinkage uit en genereer je mogelijke paren rijen tussen restaurants en restaurants_new. Beide DataFrames, pandas en recordlinkage staan klaar in je omgeving.

Deze oefening maakt deel uit van de cursus

Data opschonen in Python

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create an indexer and object and find possible pairs
indexer = ____

# Block pairing on cuisine_type
indexer.____(____)

# Generate pairs
pairs = indexer.____(____, ____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Data opschonen in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

In this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.

Exercise 1: Data type constraints Exercise 2: Common data types Exercise 3: Numeric data or ... ?Exercise 4: Summing strings and concatenating numbers Exercise 5: Data range constraints Exercise 6: Tire size constraints Exercise 7: Back to the future Exercise 8: Uniqueness constraints Exercise 9: How big is your subset?Exercise 10: Finding duplicates Exercise 11: Treating duplicates

Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.

Exercise 1: Membership constraints Exercise 2: Members only Exercise 3: Finding consistency Exercise 4: Categorical variables Exercise 5: Categories of errors Exercise 6: Inconsistent categories Exercise 7: Remapping categories Exercise 8: Cleaning text data Exercise 9: Removing titles and taking names Exercise 10: Keeping it descriptive

In this chapter, you'll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You'll also gain invaluable skills that will help you verify that values have been added correctly, and that missing values don't negatively impact your analyses.

Exercise 1: Uniformity Exercise 2: Ambiguous dates Exercise 3: Uniform currencies Exercise 4: Uniform dates Exercise 5: Cross field validation Exercise 6: Cross field or no cross field?Exercise 7: How's our data integrity?Exercise 8: Completeness Exercise 9: Is this missing at random?Exercise 10: Missing investors Exercise 11: Follow the money

Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you'll then use your new skills to join two restaurant review datasets into one clean master dataset.

Exercise 1: Strings vergelijken Exercise 2: Minimale bewerkingsafstand Exercise 3: Het afkappunt Exercise 4: Categorieën opnieuw toewijzen II Exercise 5: Paren genereren Exercise 6: Koppelen of niet koppelen?Exercise 7: Paren van restaurants

Huidige oefening

Exercise 8: Vergelijkbare restaurants Exercise 9: DataFrames koppelen Exercise 10: De juiste index ophalen Exercise 11: Alles aan elkaar koppelen!Exercise 12: Gefeliciteerd!